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      <div class="helptext"><pre><!--helptext -->  <span class="helptopic">prtClassRvmFigueiredo</span>  Relevance vector machine classifier using a Jefferey's prior
 
     CLASSIFIER = <span class="helptopic">prtClassRvmFigueiredo</span> returns a relevance vector 
     machine classifier.
 
     CLASSIFIER = <span class="helptopic">prtClassRvmFigueiredo</span>(PROPERTY1, VALUE1, ...) constructs a
     prtClassRvm object CLASSIFIER with properties as specified by
     PROPERTY/VALUE pairs.
 
     A <span class="helptopic">prtClassRvmFigueiredo</span> object inherits all properties from the
     abstract class prtClass. In addition is has the following
     properties:
 
     kernels            - A cell array of prtKernel objects specifying
                          the kernels to use
     verbosePlot        - Flag indicating whether or not to plot during
                          training
     verboseText        - Flag indicating whether or not to output
                          verbose updates during training
     learningMaxIterations  - The maximum number of iterations
 
     A prtClassRvmFigeiredo also has the following read-only properties:
 
     learningConverged  - Flag indicating if the training converged
     beta               - The regression weights, estimated during training
     sparseBeta         - The sparse regression weights, estimated during
                         training
     sparseKernels      - The sparse regression kernels, estimated during
                         training
 
    For more informatoin on the Figueiredo algorithm, please refer to
    the following reference:
  
    M. Figueiredo, Adaptive sparseness for supervised learning, 
    IEEE PAMI, vol. 25, no. 9 pp.1150-1159, September 2003.  
 
    Training using the Figueiredo algorithm can provide faster
    and more robust convergence under some circumstances.
 
    A prtClassRvm object inherits the TRAIN, RUN, CROSSVALIDATE and
    KFOLDS methods from prtAction. It also inherits the PLOT method
    from prtClass.
 
    % Example
    TestDataSet = prtDataGenUnimodal;      % Create some test and
    TrainingDataSet = prtDataGenUnimodal;  % training data
     %    % Create a classifier with verbose plotting
    classifier = <span class="helptopic">prtClassRvmFigueiredo</span>('verbosePlot',true); 
    classifier = classifier.train(TrainingDataSet);    % Train
    classified = run(classifier, TestDataSet);         % Test
    % Plot the results
    subplot(2,1,1);
    classifier.plot;
    subplot(2,1,2);
    % figure
    [pf,pd] = prtScoreRoc(classified,TestDataSet);
    h = plot(pf,pd,'linewidth',3);
    title('ROC'); xlabel('Pf'); ylabel('Pd');</pre></div><!--after help --><!--seeAlso--><div class="footerlinktitle">See also</div><div class="footerlink"> <a href="./../prtClass.html">prtClass</a>, <a href="./../prtClassRvm.html">prtClassRvm</a>, prtClassRvnSequential,
    <a href="./../prtRegressRvmSequential.html">prtRegressRvmSequential</a>
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